Senior Data Engineer

The Lead Data Engineer will design, build, and operationalize scalable data solutions to support enterprise analytics and AI/ML initiatives. This role requires expert-level proficiency in Databricks, Azure Fabric, PySpark, SQL, and the Azure ecosystem, with deep experience across data warehouses, data lakes, and real-time integration. The Lead Data Engineer will architect end-to-end pipelines using industry-standard tools, drive automation, and move solutions effectively into production. The incumbent will ensure compliance with data governance requirements (including GxP and HIPAA/GDPR) while building reusable, integrated pipelines and analytical models that promote self-service analytics. This role provides technical leadership across the team, mentors junior engineers, and partners with business stakeholders to align data engineering with organizational objectives.

About the Role

. Data Architecture & Engineering

• Architect, design, and implement end-to-end data solutions using Azure Databricks, PySpark, Azure Data Factory, and Azure SQL.
• Design, build, and maintain data pipelines from data sources through integration to consumption for specific use cases.
• Implement robust data modeling standards across bronze, silver, and gold layers in the data lake.
• Develop data models (conceptual, logical, and/or physical) as required.
• Optimize Spark and SQL workloads for performance, scalability, and cost efficiency.
• Manage metadata using data preparation, integration, and AI-enabled tools and techniques.

. Data Integration & Automation

• Drive automation in data integration; recommend and lead implementation of techniques to automate repeatable data preparation and integration tasks.
• Build API-based integrations (REST/JSON) and real-time ingestion frameworks.
• Automate data workflows using Azure DevOps pipelines and Git-based CI/CD practices.
• Implement parameterized, reusable pipeline templates for ingestion and transformation.
• Develop automated unit, regression, and integration testing frameworks for data jobs.

. Analytics & Data Enablement

• Prepare and curate high-quality datasets for BI, reporting, and advanced analytics.
• Partner with analytics teams using Power BI, Tableau, or similar platforms to define semantic models and KPIs.
• Implement performance-optimized data models for self-service analytics.
• Will occasionally provide support to end users on the use of data visualization solutions.

Stakeholder Engagement & Leadership

• Lead technical design reviews, mentor junior engineers, and promote best practices.
• Assist cross-functional groups, business analysts, and stakeholders to gather, define, and refine data requirements.
• Collaborate with business and IT stakeholders to align data engineering with organizational objectives.
• Propose innovative data ingestion, preparation, and integration techniques to address stakeholder requirements.
• Contribute to architectural roadmaps and technology evaluations for the data platform.
• In collaboration with functional leaders, identify inefficiencies and recommend improvements to the executive team.

About You

Job Experience & Education Requirements:

Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field (Master’s preferred)

And

5–8 years of experience designing and developing enterprise-scale data solutions (data warehouses, data lakes, operational databases)

Other:

• Expert-level proficiency in Databricks, Azure Fabric, PySpark, SQL, and Azure DevOps.
• Proven experience with Azure Data Factory, ADLS Gen2, and Azure SQL Server.
• Strong experience with Microsoft Azure data management architectures including Data Warehouse, Data Lake, and Data Catalogue, and supporting processes such as Data Integration, Governance, and Metadata Management.
• Experience with Power BI required; Tableau or Looker a plus.
• Working knowledge of CI/CD automation, version control (Git), and infrastructure as code (ARM, Bicep, or Terraform).
• Experience in life sciences or healthcare industries is a strong plus.
• Good understanding of GxP, GDPR/HIPAA, and applicable CFR/CTR/CTD regulations.
• Demonstrated success working with both IT and business stakeholders while integrating analytics and data science output into business processes and workflows.
• Must have excellent written and verbal communication skills.
• Proven ability to work independently and as part of a team and meet important deadlines.
• Statistical analysis skills are an asset.

Originally posted on Himalayas